first commit

.gitignore

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+*/__pycache__
+pretrained_weights/isecret.pt
+pretrained_weights/led.bin
+pretrained_weights/pcenet.pth

LICENSE

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README.md

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+# Learning Enhancement From Degradation: A Diffusion Model For Fundus Image Enhancement
+
+The official implementation of the paper ``Learning Enhancement From Degradation: A Diffusion Model For Fundus Image Enhancement``. The related paper link will be updated soon.
+
+
+![image info](./docs/led.gif)
+
+## Highlights
+- ### Continuous and reliable enhancement
+![image info](./docs/Continuous.png)
+
+- ### Flexable and ffectively integrated into any SOTA
+![image info](./docs/OC.png)
+
+![image info](./docs/vessels.png)
+
+- ### Robust for OOD low-quality images
+![image info](./docs/jpeg_loss.png)
+
+![image info](./docs/mf_loss.png)
+
+- ### SOTA performance
+![image info](./docs/performance.png)
+
+Start LED with few lines
+
+```python
+from led.pipelines.led_pipeline import LEDPipeline
+led = LEDPipeline()
+led.cuda()
+led_enhancement = led('./doc/example.jpeg')[0]
+```
+
+Furthermore, you can combine LED with any existing SOTA methods as external backend. Current supported backends include:
+- I-SECRET
+- PCE-Net
+
+Try
+```python
+led = LEDPipeline(backend='I-SECRET', num_cond_steps=200)
+```
+
+For more details, please read exmaple.ipynb.
+
+## Catalog
+- [ ] Training guidance
+- [ ] Support for ArcNet and SCRNet
+- [ ] Add related codes for data-driven degradation
+- [x] Inference pipeline
+
+## Train
+For training your own LED, you need to update few lines in configs/train_led.yaml
+```yaml
+    train_good_image_dir: # update to training hq images directrory
+    train_bad_image_dir: # update to training lq images directrory
+    train_degraded_image_dir: # update to training degraded images directrory
+    val_good_image_dir:  # update to validation hq images directrory
+    val_bad_image_dir: # update to validation lq images directrory
+```
+Please note that ``train_degraded_image_dir`` should contain degraded high-qualty images by any data-driven methods. We will inculde related codes in our future workspace. However, you can consider using some existing repos instead, like [CUT](https://github.com/taesungp/contrastive-unpaired-translation) or [CycleGAN](https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix).
+
+To train LED, simply  run
+```bash
+accelerate launch  --mixed_precision fp16 --gpu_ids 0 --num_processes 1 script/train.py 
+```
+More GPUs will take significant performance improvement.
+
+
+## Acknowledgement 
+Thanks for [PCENet](https://github.com/HeverLaw/PCENet-Image-Enhancement), [ArcNet](https://github.com/liamheng/Annotation-free-Fundus-Image-Enhancement) and [SCRNet](https://github.com/liamheng/Annotation-free-Fundus-Image-Enhancement) for sharing their powerful pre-trained weights! Thansk for [diffusers](https://github.com/huggingface/diffusers) for sharing codes.
+
+## Citation
+
+If this work is helpful for your research, please consider citing the following BibTeX entry.
+
+```
+Will be updated 
+```
+## License
+This repository is released under the Apache 2.0 license as found in the [LICENSE](LICENSE) file.

configs/train_led.yaml

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+seed: 42
+
+# data
+data:
+  train_good_image_dir: # high quality images
+  train_bad_image_dir: # low quality images
+  train_degraded_image_dir: # degraded images
+  val_good_image_dir: # high quality images
+  val_bad_image_dir: # low quality images
+  image_size: 512
+
+# model
+model:
+  sample_size: ${data.image_size}
+  in_channels: 6
+  out_channels: 3
+  center_input_sample: false
+  time_embedding_type: positional
+  freq_shift: 0
+  flip_sin_to_cos: true
+  down_block_types:
+  - DownBlock2D
+  - DownBlock2D
+  - DownBlock2D
+  - DownBlock2D
+  - AttnDownBlock2D
+  - DownBlock2D
+  up_block_types:
+  - UpBlock2D
+  - AttnUpBlock2D
+  - UpBlock2D
+  - UpBlock2D
+  - UpBlock2D
+  - UpBlock2D
+  block_out_channels:
+  - 128
+  - 128
+  - 256
+  - 256
+  - 512
+  - 512
+  layers_per_block: 2
+  mid_block_scale_factor: 1
+  downsample_padding: 1
+  act_fn: silu
+  attention_head_dim: 8
+  norm_num_groups: 32
+  norm_eps: 1.0e-05
+  resnet_time_scale_shift: default
+  add_attention: true
+
+
+# training
+train:
+  lr: 1e-5
+  lr_warmup_steps: 100
+  adam_beta1: 0.95
+  adam_beta2: 0.999
+  adam_eps: 1e-08
+  weight_decay: 1e-6
+  gradient_accumulation_steps: 1
+  num_epochs: 150
+  ema_max_decay: 0.9999
+  checkpointing_steps_total_limit: None
+  ema_inv_gamma: 1.0
+  ema_power: 0.75
+  checkpointing_steps: 1000
+  save_images_epochs: 5
+  adv_start_epoch: 5
+  save_model_epochs: 50
+
+diffusion:
+  num_train_steps: 1000
+  prediction_type: epsilon
+  num_inference_steps: 50
+  beta_schedule: linear
+  num_cond_steps: 800
+
+output_dir: './logs/LED'
+mixed_precision: 'fp16'
+gpus: 0,1,2,3,6,7
+num_worker: 8
+train_batch_size: 3
+eval_batch_size: 16
+test_batch_size: 16
+
+optimizer_name: adamw
+model_name: unet
+lr_scheduler_name: cosine
+use_ema: true
+logger_name: tensorboard

led/__init__.py

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+from led.pipelines.led_pipeline import LEDPipeline